With the continued development in computer application technology, motion recognition has been widely used in industries such as sports, games, movies, medical simulation and motion and skill training, etc. For example, when a user is playing, for example, golf or badminton, a data acquisition device comprising a sensor may be configured in a club or glove to capture the motion data and send the captured motion data to a motion computing device, which may be disposed in an intelligent terminal including a mobile phone and a tablet computer. After computation and analysis with respect to the motion data from the data acquisition device, the motion computing device may obtain data regarding the position and posture while the user is playing to allow the user to share data or acquire motion training, etc.
One of the conventional motion recognition systems is shown in FIG. 1, which includes a data acquisition device and a motion computing device, as introduced above. The data acquisition device mainly comprises a sensor and a communications module. The motion data collected at the sensor is transmitted in real time to the motion computing device via the communications module. The sensor mentioned herein includes, but is not limited to, an acceleration sensor, gyroscope, and a magnetic field sensor, etc.
As is known, the data acquisition device and the motion computing device generally communicate motion data in real time over a wireless channel, and the motion computing device uses acceleration-based integral algorithms to compute the motion. Consequently, when subjected to the same amount of noises, the higher the sampling rate, the more accurate the computed position and posture data. Nonetheless, in the case of a high sampling rate, the huge amount of data collected by the sensor will exert too much pressure on the transmission over the wireless channel. This not only results in large wireless power consumption, but also goes beyond the maximum capacity of the wireless channel.